DETAILED ACTION
This Office action is in response to the application filed on 22 January 2024.
Claims 1-4 are presented for examination.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4 are rejected under 35 U.S.C. 103 as being unpatentable over HEIN et al. DE 10 2016 005 513 A1 (published 02 November 2017) in view of Kleider et al. US 2020/0252318 A1.
As to claim 1, HEIN discloses substantially the invention as claimed, including a transmission space reproduction method (Abstract, “the present invention is a device and a method for checking the functionality and reliability of a particularly large electrical radio system are provided with which the statistical properties of real radio channel in its essential properties in the laboratory can be replicated reproducibly with significantly reduced hardware and software effort”’..) comprising:
simulating while sequentially changing a parameter in a reverberation chamber (an absorber-lined shielding chamber (1), claim 1) used to reproduce propagation characteristics so as to calculate propagation characteristics in the reverberation chamber (Figure 1, and associated text, “the simulation of the downlink (transmission direction from the remote to the “System Under Test” SUT… “ [3] or in English (page 2, lines 1-9, lines 38-41); “varied variation of the spreads of the parameters listed in points 2-4, including deterministic methods or stochastic distributions – temporal variation of the parameters listed under points 1 to 5, e.g., B, by mechanical movement or electronic control of the reflectors”, [8], [15] or in English (page 2 lines 38-41; page 3, lines 43-45);
However HEIN does not explicitly disclose the claimed elements of “forming a learning model for a calculated parameter by machine learning using actually measured propagation characteristics and parameters”; “generating a parameter corresponding to propagation characteristics to be reproduced using the formed learning model”; and “controlling a channel emulator based on the generated parameter so as to perform processing of forming a transmission space having the propagation characteristics to be reproduced in the reverberation chamber”.
Kleider discloses in Figures 1-2 and associated paragraphs that, “forming a learning model (a learning algorithm) for a calculated parameter by machine learning (a machine learning system 24 having the parameter extraction processor 102) using actually measured propagation characteristics and parameters”; “(the parameter extraction processor 102) generating (extracting) a parameter corresponding to propagation characteristics to be reproduced using the formed learning model”; and “controlling a channel emulator (the channel emulator 104) based on the generated parameter so as to perform processing of forming a transmission space having the propagation characteristics to be reproduced in the reverberation chamber” (Figs 1-3, and associated paragraphs, [26]-[27], [32]-[35]; [36]-[43]).
Accordingly, it would have been obvious to one of ordinary skill in the AI/ML/deep learning art before the effective filing date of the claimed to have modified Kleider’s teachings of the machine learning system with the teachings of Hein’s, for the purpose of providing at least one synthesized channel parameter to produce and store perturbed signal and artificial intelligence systems (Kleider, Abstract).
As to claim 2, HEIN-Kleider discloses, wherein in simulating, the simulation is performed while further changing a parameter of a dynamic reflector that is provided in the reverberation chamber and is capable of controlling phases of arrival waves when reflecting radio waves so as to calculate the propagation characteristics in the reverberation chamber (HEIN, [10]-[15] or in English (page 2, line 42 - page 3, line 45).
Claims 3, 4 correspond to the device claims of the method claims 1, 2; therefore, they are rejected under the same rationale as in the method claims 1, 2 shown above.
The prior art cited in this Office action are: HEIN et al. DE 10 2016 005 513 A1 (published 02 November 2017); Kleider et al. US 2020/0252318 A1.
Conclusion
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/HAI V NGUYEN/Primary Examiner, Art Unit 2649